

<!DOCTYPE html>
<html class="writer-html5" lang="en" data-content_root="./">
<head>
  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />

  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
  <title>Examples &mdash; CodeCarbon 3.1.1 documentation</title>
      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />

  
      <script src="_static/jquery.js?v=5d32c60e"></script>
      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
      <script src="_static/documentation_options.js?v=796a81b5"></script>
      <script src="_static/doctools.js?v=9bcbadda"></script>
      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
    <script src="_static/js/theme.js"></script>
    <link rel="index" title="Index" href="genindex.html" />
    <link rel="search" title="Search" href="search.html" />
    <link rel="next" title="Comet Integration" href="comet.html" />
    <link rel="prev" title="Parameters" href="parameters.html" /> 
</head>

<body class="wy-body-for-nav"> 
  <div class="wy-grid-for-nav">
    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
      <div class="wy-side-scroll">
        <div class="wy-side-nav-search" >

          
          
          <a href="index.html" class="icon icon-home">
            CodeCarbon
          </a>
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>
        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
              <p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="motivation.html">Motivation</a></li>
<li class="toctree-l1"><a class="reference internal" href="methodology.html">Methodology</a></li>
<li class="toctree-l1"><a class="reference internal" href="rapl.html">RAPL Metrics</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_examples.html">Model Comparisons</a></li>
<li class="toctree-l1"><a class="reference internal" href="faq.html">Frequently Asked Questions</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="installation.html">Installing CodeCarbon</a></li>
<li class="toctree-l1"><a class="reference internal" href="usage.html">Quickstart</a></li>
<li class="toctree-l1"><a class="reference internal" href="usage.html#configuration">Configuration</a></li>
<li class="toctree-l1"><a class="reference internal" href="api.html">CodeCarbon API</a></li>
<li class="toctree-l1"><a class="reference internal" href="parameters.html">Parameters</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#using-the-decorator">Using the Decorator</a></li>
<li class="toctree-l2"><a class="reference internal" href="#using-the-context-manager">Using the Context Manager</a></li>
<li class="toctree-l2"><a class="reference internal" href="#using-the-explicit-object">Using the Explicit Object</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="comet.html">Comet Integration</a></li>
<li class="toctree-l1"><a class="reference internal" href="advanced_installation.html">Advanced Installation</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Logging</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="output.html">Output</a></li>
<li class="toctree-l1"><a class="reference internal" href="to_logger.html">Collecting emissions to a logger</a></li>
<li class="toctree-l1"><a class="reference internal" href="visualize.html">Visualize</a></li>
</ul>

        </div>
      </div>
    </nav>

    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
          <a href="index.html">CodeCarbon</a>
      </nav>

      <div class="wy-nav-content">
        <div class="rst-content">
          <div role="navigation" aria-label="Page navigation">
  <ul class="wy-breadcrumbs">
      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
      <li class="breadcrumb-item active">Examples</li>
      <li class="wy-breadcrumbs-aside">
            <a href="_sources/examples.rst.txt" rel="nofollow"> View page source</a>
      </li>
  </ul>
  <hr/>
</div>
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
             
  <section id="examples">
<span id="id1"></span><h1>Examples<a class="headerlink" href="#examples" title="Link to this heading"></a></h1>
<p>Following are examples to train a Deep Learning model on MNIST Data to recognize digits in images using TensorFlow.</p>
<section id="using-the-decorator">
<h2>Using the Decorator<a class="headerlink" href="#using-the-decorator" title="Link to this heading"></a></h2>
<p>This is the simplest way to use the CodeCarbon tracker with two lines of code. You just need to copy-paste <cite>from codecarbon import track_emissions</cite> and add the <cite>&#64;track_emissions</cite> decorator to your training function. The emissions will be tracked automatically and printed at the end of the training.</p>
<p>But you can’t get them in your code, see the Context Manager section below for that.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">codecarbon</span><span class="w"> </span><span class="kn">import</span> <span class="n">track_emissions</span>


<span class="nd">@track_emissions</span><span class="p">(</span><span class="n">project_name</span><span class="o">=</span><span class="s2">&quot;mnist&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">train_model</span><span class="p">():</span>
    <span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>
    <span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
    <span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
        <span class="p">[</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
        <span class="p">]</span>
    <span class="p">)</span>
    <span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">&quot;adam&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">])</span>

    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">model</span>


<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">()</span>
</pre></div>
</div>
</section>
<section id="using-the-context-manager">
<h2>Using the Context Manager<a class="headerlink" href="#using-the-context-manager" title="Link to this heading"></a></h2>
<p>We think this is the best way to use CodeCarbon. Still only two lines of code, and you can get the emissions in your code.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>

<span class="kn">from</span><span class="w"> </span><span class="nn">codecarbon</span><span class="w"> </span><span class="kn">import</span> <span class="n">EmissionsTracker</span>

<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>

<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>


<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
    <span class="p">[</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
    <span class="p">]</span>
<span class="p">)</span>

<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="k">with</span> <span class="n">EmissionsTracker</span><span class="p">()</span> <span class="k">as</span> <span class="n">tracker</span><span class="p">:</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">&quot;adam&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">])</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>

<span class="c1"># Display the emissions data</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Carbon emissions from computation: </span><span class="si">{</span><span class="n">tracker</span><span class="o">.</span><span class="n">final_emissions</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">1000</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> g CO2eq&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Detailed emissions data:&quot;</span><span class="p">,</span> <span class="n">tracker</span><span class="o">.</span><span class="n">final_emissions_data</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="using-the-explicit-object">
<h2>Using the Explicit Object<a class="headerlink" href="#using-the-explicit-object" title="Link to this heading"></a></h2>
<p>This is the recommended way to use the CodeCarbon tracker in a Notebook : you instantiate the tracker and call the <cite>start()</cite> method at the beginning of the Notebook. You call the stop() method at the end of the Notebook to stop the tracker and get the emissions.</p>
<p>If not in an interactive Notebook, always use a <cite>try…finally</cite> block to ensure that the tracker is stopped even if an error occurs during training.
This is important to ensure the CodeCarbon scheduler is stopped. If you don’t use <cite>try…finally</cite>, the scheduler will continue running in the background after your computation code has crashed, so your program will never finish.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>

<span class="kn">from</span><span class="w"> </span><span class="nn">codecarbon</span><span class="w"> </span><span class="kn">import</span> <span class="n">EmissionsTracker</span>

<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>

<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>


<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
    <span class="p">[</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
    <span class="p">]</span>
<span class="p">)</span>

<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">&quot;adam&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">])</span>

<span class="n">tracker</span> <span class="o">=</span> <span class="n">EmissionsTracker</span><span class="p">()</span>
<span class="n">tracker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;An error occurred: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">finally</span><span class="p">:</span>
    <span class="n">emissions</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">tracker</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">emissions</span><span class="p">)</span>
</pre></div>
</div>
<p>Other examples are available in the <a class="reference external" href="https://github.com/mlco2/codecarbon/tree/master/examples">project GitHub repository</a>.</p>
</section>
</section>


           </div>
          </div>
          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
        <a href="parameters.html" class="btn btn-neutral float-left" title="Parameters" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
        <a href="comet.html" class="btn btn-neutral float-right" title="Comet Integration" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
    </div>

  <hr/>

  <div role="contentinfo">
    <p>&#169; Copyright 2020, BCG GAMMA, Comet.ml, Haverford College, MILA.</p>
  </div>

  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
    provided by <a href="https://readthedocs.org">Read the Docs</a>.
   

</footer>
        </div>
      </div>
    </section>
  </div>
  <script>
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script> 

</body>
</html>